import os
from queue import Queue
from threading import Thread

import pandas as pd
import tensorflow as tf
import collections
import args
from bert import tokenization
from bert import modeling
from bert import optimization


# os.environ['CUDA_VISIBLE_DEVICES'] = '1'


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
          guid: Unique id for the example.
          text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
          text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
          label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_test_examples(self, data_dir):
        """Gets a collection of `InputExample`s for prediction."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()


class SimProcessor(DataProcessor):
    def get_train_examples(self, data_dir):
        file_path = os.path.join(data_dir, 'train.txt')
        train_df = pd.read_csv(file_path, encoding='utf-8', sep='\t', header=None)
        train_data = []
        for index, train in enumerate(train_df.values):
            guid = 'train-%d' % index
            text_a = tokenization.convert_to_unicode(str(train[1]))
            text_b = tokenization.convert_to_unicode(str(train[2]))
            label = str(train[3])
            train_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return train_data

    def get_dev_examples(self, data_dir):
        file_path = os.path.join(data_dir, 'dev.txt')
        dev_df = pd.read_csv(file_path, encoding='utf-8', sep='\t', header=None)
        dev_data = []
        for index, dev in enumerate(dev_df.values):
            guid = 'test-%d' % index
            text_a = tokenization.convert_to_unicode(str(dev[1]))
            text_b = tokenization.convert_to_unicode(str(dev[2]))
            label = str(dev[3])
            dev_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return dev_data

    def get_test_examples(self, data_dir):
        file_path = os.path.join(data_dir, 'test.txt')
        test_df = pd.read_csv(file_path, encoding='utf-8', sep='\t', header=None)
        test_data = []
        for index, test in enumerate(test_df.values):
            guid = 'test-%d' % index
            text_a = tokenization.convert_to_unicode(str(test[1]))
            text_b = tokenization.convert_to_unicode(str(test[2]))
            label = str(test[3])
            test_data.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
        return test_data

    def get_sentence_examples(self, questions):
        for index, data in enumerate(questions):
            guid = 'test-%d' % index
            text_a = tokenization.convert_to_unicode(str(data[0]))
            text_b = tokenization.convert_to_unicode(str(data[1]))
            label = str(0)
            yield InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)

    def get_labels(self):
        return ['0', '1']


class BertSim:

    def __init__(self, batch_size=args.batch_size):
        self.mode = None
        self.max_seq_length = args.max_seq_len
        self.tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file, do_lower_case=True)
        self.batch_size = batch_size
        self.estimator = None
        self.processor = SimProcessor()
        tf.logging.set_verbosity(tf.logging.INFO)

    def set_mode(self, mode):
        self.mode = mode
        self.estimator = self.get_estimator()
        if mode == tf.estimator.ModeKeys.PREDICT:
            self.input_queue = Queue(maxsize=1)
            self.output_queue = Queue(maxsize=1)
            self.predict_thread = Thread(target=self.predict_from_queue, daemon=True)
            self.predict_thread.start()

    def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                     labels, num_labels, use_one_hot_embeddings):
        """Creates a classification model."""
        model = modeling.BertModel(
            config=bert_config,
            is_training=is_training,
            input_ids=input_ids,
            input_mask=input_mask,
            token_type_ids=segment_ids,
            use_one_hot_embeddings=use_one_hot_embeddings)

        # In the demo, we are doing a simple classification task on the entire
        # segment.
        #
        # If you want to use the token-level output, use model.get_sequence_output()
        # instead.
        output_layer = model.get_pooled_output()

        hidden_size = output_layer.shape[-1].value

        output_weights = tf.get_variable(
            "output_weights", [num_labels, hidden_size],
            initializer=tf.truncated_normal_initializer(stddev=0.02))

        output_bias = tf.get_variable(
            "output_bias", [num_labels], initializer=tf.zeros_initializer())

        with tf.variable_scope("loss"):
            if is_training:
                # I.e., 0.1 dropout
                output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

            logits = tf.matmul(output_layer, output_weights, transpose_b=True)
            logits = tf.nn.bias_add(logits, output_bias)
            probabilities = tf.nn.softmax(logits, axis=-1)
            log_probs = tf.nn.log_softmax(logits, axis=-1)

            one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)

            per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
            loss = tf.reduce_mean(per_example_loss)

            return (loss, per_example_loss, logits, probabilities)

    def model_fn_builder(self, bert_config, num_labels, init_checkpoint, learning_rate,
                         num_train_steps, num_warmup_steps,
                         use_one_hot_embeddings):
        """Returns `model_fn` closurimport_tfe for TPUEstimator."""

        def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
            from tensorflow.python.estimator.model_fn import EstimatorSpec

            tf.logging.info("*** Features ***")
            for name in sorted(features.keys()):
                tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

            input_ids = features["input_ids"]
            input_mask = features["input_mask"]
            segment_ids = features["segment_ids"]
            label_ids = features["label_ids"]

            is_training = (mode == tf.estimator.ModeKeys.TRAIN)

            (total_loss, per_example_loss, logits, probabilities) = BertSim.create_model(
                bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
                num_labels, use_one_hot_embeddings)

            tvars = tf.trainable_variables()
            initialized_variable_names = {}

            if init_checkpoint:
                (assignment_map, initialized_variable_names) \
                    = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

            tf.logging.info("**** Trainable Variables ****")
            for var in tvars:
                init_string = ""
                if var.name in initialized_variable_names:
                    init_string = ", *INIT_FROM_CKPT*"
                tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
                                init_string)

            if mode == tf.estimator.ModeKeys.TRAIN:

                train_op = optimization.create_optimizer(
                    total_loss, learning_rate, num_train_steps, num_warmup_steps, False)

                output_spec = EstimatorSpec(
                    mode=mode,
                    loss=total_loss,
                    train_op=train_op)
            elif mode == tf.estimator.ModeKeys.EVAL:

                def metric_fn(per_example_loss, label_ids, logits):
                    predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
                    accuracy = tf.metrics.accuracy(label_ids, predictions)
                    auc = tf.metrics.auc(label_ids, predictions)
                    loss = tf.metrics.mean(per_example_loss)
                    return {
                        "eval_accuracy": accuracy,
                        "eval_auc": auc,
                        "eval_loss": loss,
                    }

                eval_metrics = metric_fn(per_example_loss, label_ids, logits)
                output_spec = EstimatorSpec(
                    mode=mode,
                    loss=total_loss,
                    eval_metric_ops=eval_metrics)
            else:
                output_spec = EstimatorSpec(mode=mode, predictions=probabilities)

            return output_spec

        return model_fn

    def get_estimator(self):

        from tensorflow.python.estimator.estimator import Estimator
        from tensorflow.python.estimator.run_config import RunConfig

        bert_config = modeling.BertConfig.from_json_file(args.config_name)
        label_list = self.processor.get_labels()
        train_examples = self.processor.get_train_examples(args.data_dir)
        num_train_steps = int(
            len(train_examples) / self.batch_size * args.num_train_epochs)
        num_warmup_steps = int(num_train_steps * 0.1)

        if self.mode == tf.estimator.ModeKeys.TRAIN:
            init_checkpoint = args.ckpt_name
        else:
            init_checkpoint = args.output_dir

        model_fn = self.model_fn_builder(
            bert_config=bert_config,
            num_labels=len(label_list),
            init_checkpoint=init_checkpoint,
            learning_rate=args.learning_rate,
            num_train_steps=num_train_steps,
            num_warmup_steps=num_warmup_steps,
            use_one_hot_embeddings=False)

        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        config.gpu_options.per_process_gpu_memory_fraction = args.gpu_memory_fraction
        config.log_device_placement = False

        return Estimator(model_fn=model_fn, config=RunConfig(session_config=config), model_dir=args.output_dir,
                         params={'batch_size': self.batch_size})

    def predict_from_queue(self):
        for i in self.estimator.predict(input_fn=self.queue_predict_input_fn, yield_single_examples=False):
            self.output_queue.put(i)

    def queue_predict_input_fn(self):
        return (tf.data.Dataset.from_generator(
            self.generate_from_queue,
            output_types={
                'input_ids': tf.int32,
                'input_mask': tf.int32,
                'segment_ids': tf.int32,
                'label_ids': tf.int32},
            output_shapes={
                'input_ids': (None, self.max_seq_length),
                'input_mask': (None, self.max_seq_length),
                'segment_ids': (None, self.max_seq_length),
                'label_ids': (1,)}).prefetch(10))

    def convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer):
        """Convert a set of `InputExample`s to a list of `InputFeatures`."""

        for (ex_index, example) in enumerate(examples):
            label_map = {}
            for (i, label) in enumerate(label_list):
                label_map[label] = i

            tokens_a = tokenizer.tokenize(example.text_a)
            tokens_b = None
            if example.text_b:
                tokens_b = tokenizer.tokenize(example.text_b)

            if tokens_b:
                # Modifies `tokens_a` and `tokens_b` in place so that the total
                # length is less than the specified length.
                # Account for [CLS], [SEP], [SEP] with "- 3"
                self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
            else:
                # Account for [CLS] and [SEP] with "- 2"
                if len(tokens_a) > max_seq_length - 2:
                    tokens_a = tokens_a[0:(max_seq_length - 2)]

            # The convention in BERT is:
            # (a) For sequence pairs:
            #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
            #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
            # (b) For single sequences:
            #  tokens:   [CLS] the dog is hairy . [SEP]
            #  type_ids: 0     0   0   0  0     0 0
            #
            # Where "type_ids" are used to indicate whether this is the first
            # sequence or the second sequence. The embedding vectors for `type=0` and
            # `type=1` were learned during pre-training and are added to the wordpiece
            # embedding vector (and position vector). This is not *strictly* necessary
            # since the [SEP] token unambiguously separates the sequences, but it makes
            # it easier for the model to learn the concept of sequences.
            #
            # For classification tasks, the first vector (corresponding to [CLS]) is
            # used as as the "sentence vector". Note that this only makes sense because
            # the entire model is fine-tuned.
            tokens = []
            segment_ids = []
            tokens.append("[CLS]")
            segment_ids.append(0)
            for token in tokens_a:
                tokens.append(token)
                segment_ids.append(0)
            tokens.append("[SEP]")
            segment_ids.append(0)

            if tokens_b:
                for token in tokens_b:
                    tokens.append(token)
                    segment_ids.append(1)
                tokens.append("[SEP]")
                segment_ids.append(1)

            input_ids = tokenizer.convert_tokens_to_ids(tokens)

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            input_mask = [1] * len(input_ids)

            # Zero-pad up to the sequence length.
            while len(input_ids) < max_seq_length:
                input_ids.append(0)
                input_mask.append(0)
                segment_ids.append(0)

            assert len(input_ids) == max_seq_length
            assert len(input_mask) == max_seq_length
            assert len(segment_ids) == max_seq_length

            label_id = label_map[example.label]
            if ex_index < 5:
                tf.logging.info("*** Example ***")
                tf.logging.info("guid: %s" % (example.guid))
                tf.logging.info("tokens: %s" % " ".join(
                    [tokenization.printable_text(x) for x in tokens]))
                tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
                tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
                tf.logging.info("label: %s (id = %d)" % (example.label, label_id))

            feature = InputFeatures(
                input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_id=label_id)

            yield feature

    def generate_from_queue(self):
        while True:
            predict_examples = self.processor.get_sentence_examples(self.input_queue.get())
            features = list(self.convert_examples_to_features(predict_examples, self.processor.get_labels(),
                                                              args.max_seq_len, self.tokenizer))
            yield {
                'input_ids': [f.input_ids for f in features],
                'input_mask': [f.input_mask for f in features],
                'segment_ids': [f.segment_ids for f in features],
                'label_ids': [f.label_id for f in features]
            }

    def _truncate_seq_pair(self, tokens_a, tokens_b, max_length):
        """Truncates a sequence pair in place to the maximum length."""

        # This is a simple heuristic which will always truncate the longer sequence
        # one token at a time. This makes more sense than truncating an equal percent
        # of tokens from each, since if one sequence is very short then each token
        # that's truncated likely contains more information than a longer sequence.
        while True:
            total_length = len(tokens_a) + len(tokens_b)
            if total_length <= max_length:
                break
            if len(tokens_a) > len(tokens_b):
                tokens_a.pop()
            else:
                tokens_b.pop()

    def convert_single_example(self, ex_index, example, label_list, max_seq_length, tokenizer):
        """Converts a single `InputExample` into a single `InputFeatures`."""
        label_map = {}
        for (i, label) in enumerate(label_list):
            label_map[label] = i

        tokens_a = tokenizer.tokenize(example.text_a)
        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)

        if tokens_b:
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[0:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0     0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = []
        segment_ids = []
        tokens.append("[CLS]")
        segment_ids.append(0)
        for token in tokens_a:
            tokens.append(token)
            segment_ids.append(0)
        tokens.append("[SEP]")
        segment_ids.append(0)

        if tokens_b:
            for token in tokens_b:
                tokens.append(token)
                segment_ids.append(1)
            tokens.append("[SEP]")
            segment_ids.append(1)

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        while len(input_ids) < max_seq_length:
            input_ids.append(0)
            input_mask.append(0)
            segment_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        label_id = label_map[example.label]
        if ex_index < 5:
            tf.logging.info("*** Example ***")
            tf.logging.info("guid: %s" % (example.guid))
            tf.logging.info("tokens: %s" % " ".join(
                [tokenization.printable_text(x) for x in tokens]))
            tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
            tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
            tf.logging.info("label: %s (id = %d)" % (example.label, label_id))

        feature = InputFeatures(
            input_ids=input_ids,
            input_mask=input_mask,
            segment_ids=segment_ids,
            label_id=label_id)
        return feature

    def file_based_convert_examples_to_features(self, examples, label_list, max_seq_length, tokenizer, output_file):
        """Convert a set of `InputExample`s to a TFRecord file."""

        writer = tf.python_io.TFRecordWriter(output_file)

        for (ex_index, example) in enumerate(examples):
            if ex_index % 10000 == 0:
                tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

            feature = self.convert_single_example(ex_index, example, label_list,
                                                  max_seq_length, tokenizer)

            def create_int_feature(values):
                f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
                return f

            features = collections.OrderedDict()
            features["input_ids"] = create_int_feature(feature.input_ids)
            features["input_mask"] = create_int_feature(feature.input_mask)
            features["segment_ids"] = create_int_feature(feature.segment_ids)
            features["label_ids"] = create_int_feature([feature.label_id])

            tf_example = tf.train.Example(features=tf.train.Features(feature=features))
            writer.write(tf_example.SerializeToString())

    def file_based_input_fn_builder(self, input_file, seq_length, is_training, drop_remainder):
        """Creates an `input_fn` closure to be passed to TPUEstimator."""

        name_to_features = {
            "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
            "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
            "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
            "label_ids": tf.FixedLenFeature([], tf.int64),
        }

        def _decode_record(record, name_to_features):
            """Decodes a record to a TensorFlow example."""
            example = tf.parse_single_example(record, name_to_features)

            # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
            # So cast all int64 to int32.
            for name in list(example.keys()):
                t = example[name]
                if t.dtype == tf.int64:
                    t = tf.to_int32(t)
                example[name] = t

            return example

        def input_fn(params):
            """The actual input function."""
            batch_size = params["batch_size"]

            # For training, we want a lot of parallel reading and shuffling.
            # For eval, we want no shuffling and parallel reading doesn't matter.
            d = tf.data.TFRecordDataset(input_file)
            if is_training:
                d = d.repeat()
                d = d.shuffle(buffer_size=100)

            d = d.apply(
                tf.contrib.data.map_and_batch(
                    lambda record: _decode_record(record, name_to_features),
                    batch_size=batch_size,
                    drop_remainder=drop_remainder))

            return d

        return input_fn

    def train(self):
        if self.mode is None:
            raise ValueError("Please set the 'mode' parameter")

        bert_config = modeling.BertConfig.from_json_file(args.config_name)

        if args.max_seq_len > bert_config.max_position_embeddings:
            raise ValueError(
                "Cannot use sequence length %d because the BERT model "
                "was only trained up to sequence length %d" %
                (args.max_seq_len, bert_config.max_position_embeddings))

        tf.gfile.MakeDirs(args.output_dir)

        label_list = self.processor.get_labels()

        train_examples = self.processor.get_train_examples(args.data_dir)
        num_train_steps = int(len(train_examples) / args.batch_size * args.num_train_epochs)

        estimator = self.get_estimator()

        train_file = os.path.join(args.output_dir, "train.tf_record")
        self.file_based_convert_examples_to_features(train_examples, label_list, args.max_seq_len, self.tokenizer,
                                                     train_file)
        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num examples = %d", len(train_examples))
        tf.logging.info("  Batch size = %d", args.batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)
        train_input_fn = self.file_based_input_fn_builder(input_file=train_file, seq_length=args.max_seq_len,
                                                          is_training=True,
                                                          drop_remainder=True)

        # early_stopping = tf.contrib.estimator.stop_if_no_decrease_hook(
        #     estimator,
        #     metric_name='loss',
        #     max_steps_without_decrease=10,
        #     min_steps=num_train_steps)

        # estimator.train(input_fn=train_input_fn, hooks=[early_stopping])
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)

    def eval(self):
        if self.mode is None:
            raise ValueError("Please set the 'mode' parameter")
        eval_examples = self.processor.get_dev_examples(args.data_dir)
        eval_file = os.path.join(args.output_dir, "eval.tf_record")
        label_list = self.processor.get_labels()
        self.file_based_convert_examples_to_features(
            eval_examples, label_list, args.max_seq_len, self.tokenizer, eval_file)

        tf.logging.info("***** Running evaluation *****")
        tf.logging.info("  Num examples = %d", len(eval_examples))
        tf.logging.info("  Batch size = %d", self.batch_size)

        eval_input_fn = self.file_based_input_fn_builder(
            input_file=eval_file,
            seq_length=args.max_seq_len,
            is_training=False,
            drop_remainder=False)

        estimator = self.get_estimator()
        result = estimator.evaluate(input_fn=eval_input_fn, steps=None)

        output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
        with tf.gfile.GFile(output_eval_file, "w") as writer:
            tf.logging.info("***** Eval results *****")
            for key in sorted(result.keys()):
                tf.logging.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))

    def predict(self, sentence1, sentence2):
        if self.mode is None:
            raise ValueError("Please set the 'mode' parameter")
        self.input_queue.put([(sentence1, sentence2)])
        prediction = self.output_queue.get()
        return prediction


if __name__ == '__main__':
    sim = BertSim()
    if args.train:
        sim.set_mode(tf.estimator.ModeKeys.TRAIN)
        sim.train()
        sim.set_mode(tf.estimator.ModeKeys.EVAL)
        sim.eval()
    if args.test:
        sim.set_mode(tf.estimator.ModeKeys.PREDICT)
        while True:
            sentence1 = input('sentence1: ')
            sentence2 = input('sentence2: ')
            predict = sim.predict(sentence1, sentence2)
            print(f'similarity：{predict[0][1]}')
